datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wikitext
Dataset Card for "wikitext"
Dataset Summary
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over
110 times larger. The WikiText dataset also features a far… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext.OpenThoughts-1k-sample
[!NOTE]
We have released a paper for OpenThoughts! See our paper here.
Open-Thoughts-1k-sample
This is a 1k sample of the OpenThoughts-114k dataset.
Open synthetic reasoning dataset with high-quality examples covering math, science, code, and puzzles!
Inspect the content with rich formatting with Curator Viewer.
Available Subsets
default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:
ds =… See the full description on the dataset page: https://huggingface.co/datasets/ryanmarten/OpenThoughts-1k-sample.standard-chess-games
[!CAUTION]
This dataset is still a work in progress and some breaking changes might occur.
Lichess Rated Standard Chess Games Dataset
Dataset Description
6,771,826,271 standard rated games, played on lichess.org, updated monthly from the database dumps.
This version of the data is meant for data analysis. If you need PGN files you can find those here. That said, once you have a subset of interest, it is trivial to convert it back to PGN as shown in the Dataset Usage… See the full description on the dataset page: https://huggingface.co/datasets/Lichess/standard-chess-games.super_glue
Dataset Card for "super_glue"
Dataset Summary
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
axb
Size of downloaded dataset files: 0.03 MB
Size of… See the full description on the dataset page: https://huggingface.co/datasets/aps/super_glue.preprocessed_commoncatalog-cc-byI also seperately provide just the prompts in prompts.json
keys are the image_id, and the values are the captions generated
Captions generated by moondream: vikhyatk/moondream2
Latents generated by SDXL VAE: madebyollin/sdxl-vae-fp16-fix
Embeddings generated by SigLIP: hf-hub:timm/ViT-SO400M-14-SigLIP-384
Original dataset: common-canvas/commoncatalog-cc-by
Latents f32 and embeddings are f16 bytes
Compute cost: 16x3090 for 3 day. Approximately.
physics-course-vidssciq
Dataset Card for "sciq"
Dataset Summary
The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/allenai/sciq.cybergymsquad
Dataset Card for SQuAD
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles.
Supported Tasks and Leaderboards
Question… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.SWE-bench_VerifiedDataset Summary
SWE-bench Verified is a subset of 500 samples from the SWE-bench test set, which have been human-validated for quality. SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. See this post for more details on the human-validation process.
The dataset collects 500 test Issue-Pull Request pairs from popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The original… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified.SWE-smith
SWE-smith Dataset
Code
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Paper
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Site
[12/14/2025] NOTE: We will no longer actively update this dataset.
While this dataset is still functional and usable, we recommend you use the `SWE-bench/SWE-smith-[lang]` datasets.
For better maintainability and ease-of-use, we are maintaining language-specific datasets in lieu of this mono-repo.
The SWE-smith Dataset is a training dataset of 50137 task instances from 128 GitHub repositories, collected using the SWE-smith toolkit.… See the full description on the dataset page: https://huggingface.co/datasets/SWE-bench/SWE-smith.imdb
Dataset Card for "imdb"
Dataset Summary
Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.stack-v3-train
🥞 The Stack v3
What is it?
What is being released
How to download and use it
Dataset statistics
Dataset structure
Dataset creation
Considerations for using the data
Additional information
What is it?
The Stack v3 is the largest, most up-to-date open dataset of source code, crawled directly from GitHub and built to pre-train code LLMs with full-repository context. It is the successor to The Stack v2 and, like its predecessor, is released to make the training… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train.swag
Dataset Card for Situations With Adversarial Generations
Dataset Summary
Given a partial description like "she opened the hood of the car,"
humans can reason about the situation and anticipate what might come
next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations)
is a large-scale dataset for this task of grounded commonsense
inference, unifying natural language inference and physically grounded reasoning.
The dataset consists of 113k… See the full description on the dataset page: https://huggingface.co/datasets/allenai/swag.AI-CUDA-Engineer-Archive
The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition
We release The AI CUDA Engineer archive, a dataset consisting of approximately 30,000 CUDA kernels generated by The AI CUDA Engineer. It is released under the CC-By-4.0 license and can be accessed via HuggingFace and interactively visualized here. The dataset is based on the Kernel tasks provided in KernelBench and includes a torch reference implementation, torch, NCU and Clang-tidy… See the full description on the dataset page: https://huggingface.co/datasets/SakanaAI/AI-CUDA-Engineer-Archive.riddle_senseSWE-bench_VerifiedDataset Summary
SWE-bench Verified is a subset of 500 samples from the SWE-bench test set, which have been human-validated for quality. SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. See this post for more details on the human-validation process.
The dataset collects 500 test Issue-Pull Request pairs from popular Python repositories. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The original… See the full description on the dataset page: https://huggingface.co/datasets/SWE-bench/SWE-bench_Verified.dataset_with_scriptThis is a test dataset.SWE-rebench-V2
SWE-rebench-V2
Dataset Summary
SWE-rebench-V2 is a curated dataset of software-engineering tasks derived from real GitHub issues and pull requests. The dataset contains 32,079 samples covering Python, Go, TypeScript, JavaScript, Rust, Java, PHP, Kotlin, Julia, Elixir, Scala, Swift, Dart, C, C++, C#, R, Clojure, OCaml, and Lua.
For log parser functions, base Dockerfiles, and the prompts used, please see https://github.com/SWE-rebench/SWE-rebench-V2The detailed technical… See the full description on the dataset page: https://huggingface.co/datasets/nebius/SWE-rebench-V2.Scientific-Summaries
Scientific Summaries
22 million LLM-generated structured summaries of scientific papers, enriched with OpenAlex scholarly metadata. Each paper has an 18-field structured summary covering methodology, key results, claims, limitations, and more. This public dataset includes full paper text for ~5.3 million papers where open-access status has been confirmed -- either through OpenAlex metadata or because the paper originates from a permissively licensed source such as the arXiv preprint… See the full description on the dataset page: https://huggingface.co/datasets/laion/Scientific-Summaries.datacomp200m
Datacomp200m
This is a smaller version of the datacomp_1b dataset.
Filtering was done by taking all rows that had self similarity (inner product) above 0.32. This resulted in 213009083 (213 million) rows.
The results of the datacomp paper suggest that filtering by CLIP score is better than random sampling.
Included in this repo are search indices created using autofaiss, over the text and image embeddings. There are two ways to access metadata, either in .parquet files in the… See the full description on the dataset page: https://huggingface.co/datasets/adams-story/datacomp200m.MegaPairs-Standard
MegaPairs-Standard (Standardized Version)
Dataset Summary
This is a standardized, high-efficiency version of the JUNJIE99/MegaPairs dataset.
Why use this version?
The original dataset is distributed as a massive Tar archive containing millions of images, accompanied by a separate JSONL annotation file.
The Problem: Using the original format requires extracting terabytes of small files (which can exhaust disk inodes) or writing complex logic to read from archives. It… See the full description on the dataset page: https://huggingface.co/datasets/86Cao/MegaPairs-Standard.Open-Sora-Plan-v1.1.0
Annotation
We resized the dataset to 1080p for easier uploading. Therefore, the original annotation file might not match the video names. Please refer to this https://github.com/PKU-YuanGroup/Open-Sora-Plan/issues/312#issuecomment-2197312973
Pexels
Pexels consists of multiple folders, but each folder exceeds the size limit for Huggingface uploads. Therefore, we divided each folder into 5 parts. You need to merge the 5 parts of each folder first, and then extract each… See the full description on the dataset page: https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0.snli
Dataset Card for SNLI
Dataset Summary
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE).
Supported Tasks and Leaderboards
Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/snli.squad_v2
Dataset Card for SQuAD 2.0
Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2.SVAMPsiqa
Dataset Card for "siqa"
More Information needed
scitail
Dataset Card for "scitail"
Dataset Summary
The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
crowdsource the annotation of such premise-hypothesis pair as supports… See the full description on the dataset page: https://huggingface.co/datasets/allenai/scitail.lotsa_data
LOTSA Data
The Large-scale Open Time Series Archive (LOTSA) is a collection of open time series datasets for time series forecasting.
It was collected for the purpose of pre-training Large Time Series Models.
See the paper and codebase for more information.
Citation
If you're using LOTSA data in your research or applications, please cite it using this BibTeX:
BibTeX:
@article{woo2024unified,
title={Unified Training of Universal Time Series Forecasting Transformers}… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/lotsa_data.SWE-bench_Lite
Dataset Summary
SWE-bench Lite is subset of SWE-bench, a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 300 test Issue-Pull Request pairs from 11 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Want to run inference now?
This dataset only contains the… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/SWE-bench_Lite.